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layers.py
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layers.py
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import tensorflow as tf
def lrelu(x, leak=0.2, name="lrelu", alt_relu_impl=False):
with tf.variable_scope(name):
if alt_relu_impl:
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * abs(x)
else:
return tf.maximum(x, leak * x)
def instance_norm(x):
with tf.variable_scope("instance_norm"):
out = tf.contrib.layers.instance_norm(x)
return out
def batch_norm(x, is_training = True):
with tf.variable_scope("batch_norm"):
return tf.contrib.layers.batch_norm(x, is_training=is_training, decay=0.90, scale=True, center=True,
variables_collections=["internal_batchnorm_variables"],
updates_collections=None)
def general_conv2d(inputconv, o_d=64, f_h=7, f_w=7, s_h=1, s_w=1, stddev=0.01,
padding="VALID", name="conv2d", do_norm=True, do_relu=True, keep_rate=None,
relufactor=0, norm_type=None, is_training=True):
with tf.variable_scope(name):
conv = tf.contrib.layers.conv2d(
inputconv, o_d, f_w, s_w, padding,
activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(
stddev=stddev
),
biases_initializer=None
)
if not keep_rate is None:
conv = tf.nn.dropout(conv, keep_rate)
if do_norm:
if norm_type is None:
print "normalization type is not specified!"
quit()
elif norm_type=='Ins':
conv = instance_norm(conv)
elif norm_type=='Batch':
conv = batch_norm(conv, is_training)
if do_relu:
if(relufactor == 0):
conv = tf.nn.relu(conv, "relu")
else:
conv = lrelu(conv, relufactor, "lrelu")
return conv
def general_conv2d_ga(inputconv, o_d=64, f_h=7, f_w=7, s_h=1, s_w=1, stddev=0.02,
padding="VALID", name="conv2d", do_norm=True, do_relu=True, keep_rate=None,
relufactor=0, norm_type=None, is_training=True):
with tf.variable_scope(name):
conv = tf.contrib.layers.conv2d(
inputconv, o_d, f_w, s_w, padding,
activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(
stddev=stddev
),
biases_initializer=tf.constant_initializer(0.0)
)
if not keep_rate is None:
conv = tf.nn.dropout(conv, keep_rate)
if do_norm:
if norm_type is None:
print "normalization type is not specified!"
quit()
elif norm_type=='Ins':
conv = instance_norm(conv)
elif norm_type=='Batch':
conv = batch_norm(conv, is_training)
if do_relu:
if(relufactor == 0):
conv = tf.nn.relu(conv, "relu")
else:
conv = lrelu(conv, relufactor, "lrelu")
return conv
def dilate_conv2d(inputconv, i_d=64, o_d=64, f_h=7, f_w=7, rate=2, stddev=0.01,
padding="VALID", name="dilate_conv2d", do_norm=True, do_relu=True, keep_rate=None,
relufactor=0, norm_type=None, is_training=True):
with tf.variable_scope(name):
f_1 = tf.get_variable('weights', [f_h, f_w, i_d, o_d], initializer=tf.truncated_normal_initializer(stddev=stddev))
b_1 = tf.get_variable('biases', [o_d], initializer=tf.constant_initializer(0.0, tf.float32))
di_conv_2d = tf.nn.atrous_conv2d(inputconv, f_1, rate=rate, padding=padding)
if not keep_rate is None:
di_conv_2d = tf.nn.dropout(di_conv_2d, keep_rate)
if do_norm:
if norm_type is None:
print "normalization type is not specified!"
quit()
elif norm_type=='Ins':
di_conv_2d = instance_norm(di_conv_2d)
elif norm_type=='Batch':
di_conv_2d = batch_norm(di_conv_2d, is_training)
if do_relu:
if(relufactor == 0):
di_conv_2d = tf.nn.relu(di_conv_2d, "relu")
else:
di_conv_2d = lrelu(di_conv_2d, relufactor, "lrelu")
return di_conv_2d
def general_deconv2d(inputconv, outshape, o_d=64, f_h=7, f_w=7, s_h=1, s_w=1,
stddev=0.02, padding="VALID", name="deconv2d",
do_norm=True, do_relu=True, relufactor=0, norm_type=None, is_training=True):
with tf.variable_scope(name):
conv = tf.contrib.layers.conv2d_transpose(
inputconv, o_d, [f_h, f_w],
[s_h, s_w], padding,
activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
biases_initializer=tf.constant_initializer(0.0)
)
if do_norm:
if norm_type is None:
print "normalization type is not specified!"
quit()
elif norm_type=='Ins':
conv = instance_norm(conv)
elif norm_type=='Batch':
conv = batch_norm(conv, is_training)
if do_relu:
if(relufactor == 0):
conv = tf.nn.relu(conv, "relu")
else:
conv = lrelu(conv, relufactor, "lrelu")
return conv